openml-datasets

The resources for this dataset can be found at https://www.openml.org/d/1063

Author: Mike Chapman, NASA
Source: tera-PROMISE - 2004
Please cite: Sayyad Shirabad, J. and Menzies, T.J. (2005) The PROMISE Repository of Software Engineering Databases. School of Information Technology and Engineering, University of Ottawa, Canada.

KC2 Software defect prediction
One of the NASA Metrics Data Program defect data sets. Data from software for science data processing. Data comes from McCabe and Halstead features extractors of source code. These features were defined in the 70s in an attempt to objectively characterize code features that are associated with software quality.

Attribute Information

1. loc : numeric % McCabe's line count of code
2. v(g) : numeric % McCabe "cyclomatic complexity"
3. ev(g) : numeric % McCabe "essential complexity"
4. iv(g) : numeric % McCabe "design complexity"
5. n : numeric % Halstead total operators + operands
6. v : numeric % Halstead "volume"
7. l : numeric % Halstead "program length"
8. d : numeric % Halstead "difficulty"
9. i : numeric % Halstead "intelligence"
10. e : numeric % Halstead "effort"
11. b : numeric % Halstead
12. t : numeric % Halstead's time estimator
13. lOCode : numeric % Halstead's line count
14. lOComment : numeric % Halstead's count of lines of comments
15. lOBlank : numeric % Halstead's count of blank lines
16. lOCodeAndComment: numeric
17. uniq_Op : numeric % unique operators
18. uniq_Opnd : numeric % unique operands
19. total_Op : numeric % total operators
20. total_Opnd : numeric % total operands
21. branchCount : numeric % of the flow graph
22. problems : % module has/has not one or more reported defects

Relevant papers

• Shepperd, M. and Qinbao Song and Zhongbin Sun and Mair, C. (2013) Data Quality: Some Comments on the NASA Software Defect Datasets, IEEE Transactions on Software Engineering, 39.

• Tim Menzies and Justin S. Di Stefano (2004) How Good is Your Blind Spot Sampling Policy? 2004 IEEE Conference on High Assurance Software Engineering.

• T. Menzies and J. DiStefano and A. Orrego and R. Chapman (2004) Assessing Predictors of Software Defects", Workshop on Predictive Software Models, Chicago